2 papers
stat.CO2026
Stochastic Neural Networks for Causal Inference with Missing Confounders
Yaxin Fang, Faming Liang
Unmeasured confounding is a fundamental obstacle to causal inference from observational data. Latent-variable methods address this challenge by imputing unobserved confounders, yet…
stat.ML2024
Causal-StoNet: Causal Inference for High-Dimensional Complex Data
Yaxin Fang, Faming Liang
With the advancement of data science, the collection of increasingly complex datasets has become commonplace. In such datasets, the data dimension can be extremely high, and the un…